For Fitting Markov Switching Models with package MSwM (function msmFit) there is 'control' argument which is list of control parameters.
Syntax of msmFit is:
msmFit(object, k, sw, p, data, family, control)
The 'control' argument is a list that can supply any of the following components:
-trace: A logical value. If it is TRUE, tracing information on the progress of the optimization is produced.
-maxiter: The maximum number of iterations in the EM method. Default is 100.
and so on.
My question is how to specify for example '-maxiter'? I tried: component(maxiter=50), component-maxiter=50, component[["maxiter"]]=50. Everything gives an error. "unexpected '='" or other errors connected to argument.
Format your call something like this:
mod=msmFit(model,k=2,sw=rep(TRUE,8),control=list(maxiter=10))
Related
I am trying to create a person item map that organizes the questions from a dataset in order of difficulty. I am using the eRm package and the output should looks like follows:
[person-item map] (https://hansjoerg.me/post/2018-04-23-rasch-in-r-tutorial_files/figure-html/unnamed-chunk-3-1.png)
So one of the previous steps, before running the function that outputs the map, I have to fit the data set to have a matrix which is the object that the plotting functions uses to create the actual map, but I am having an error when creating that matrix
I have already tried to follow and review some documentation that might be useful if you want to have some extra-information:
[Tutorial] https://hansjoerg.me/2018/04/23/rasch-in-r-tutorial/#plots
[Ploting function] https://rdrr.io/rforge/eRm/man/plotPImap.html
[Documentation] https://eeecon.uibk.ac.at/psychoco/2010/slides/Hatzinger.pdf
Now, this is the code that I am using. First, I install and load the respective libraries and the data:
> library(eRm)
> library(ltm)
Loading required package: MASS
Loading required package: msm
Loading required package: polycor
> library(difR)
Then I fit the PCM and generate the object of class Rm and here is the error:
*the PCM function here is specific for polytomous data, if I use a different one the output says that I am not using a dichotomous dataset
> res <- PCM(my.data)
>Warning:
The following items have no 0-responses:
AUT_10_04 AUN_07_01 AUN_07_02 AUN_09_01 AUN_10_01 AUT_11_01 AUT_17_01
AUT_20_03 CRE_05_02 CRE_07_04 CRE_10_01 CRE_16_02 EFEC_03_07 EFEC_05
EFEC_09_02 EFEC_16_03 EVA_02_01 EVA_07_01 EVA_12_02 EVA_15_06 FLX_04_01
... [rest of items]
>Responses are shifted such that lowest
category is 0.
Warning:
The following items do not have responses on
each category:
EFEC_03_07 LC_07_03 LC_11_05
Estimation may not be feasible. Please check
data matrix
I must clarify that all the dataset has a range from 1 to 5. Is a Likert polytomous dataset
Finally, I try to use the plot function and it does not have any output, the system just keep loading ad-infinitum with no answer
>plotPImap(res, sorted=TRUE)
I would like to add the description of that particular function and the arguments:
>PCM(X, W, se = TRUE, sum0 = TRUE, etaStart)
#X
Input data matrix or data frame with item responses (starting from 0);
rows represent individuals, columns represent items. Missing values are
inserted as NA.
#W
Design matrix for the PCM. If omitted, the function will compute W
automatically.
#se
If TRUE, the standard errors are computed.
#sum0
If TRUE, the parameters are normed to sum-0 by specifying an appropriate
W.
If FALSE, the first parameter is restricted to 0.
#etaStart
A vector of starting values for the eta parameters can be specified. If
missing, the 0-vector is used.
I do not understand why is necessary to have a score beginning from 0, I think that that what the error is trying to say but I don't understand quite well that output.
I highly appreciate any hint that you can provide me
Feel free to ask for any information that could be useful to reach the solution to this issue
The problem is not caused by the fact that there are no items with 0-responses. The model automatically corrects this by centering the response scale categories on zero. (You'll notice that the PI-map that you linked to is centered on zero. Also, I believe the map you linked to is of dichotomous data. Polytomous data should include the scale categories on the PI-map, I believe.)
Without being able to see your data, it is impossible to know the exact cause though.
It may be that the model is not converging. That may be what this error was alluding to: Estimation may not be feasible. Please check data matrix. You could check by entering > res at the prompt. If the model was able to converge you should see something like:
Conditional log-likelihood: -2.23709
Number of iterations: 27
Number of parameters: 8
...
Does your data contain answers with decimal numbers? I found the same error, I solved it by using dplyr::dense_rank() function:
df_ranked <- sapply(df_decimal_data, dense_rank)
Worked.
I am currently using the partial_dependence function within the edarf package.
Regarding the n parameter, i found the below definition from https://rdrr.io/cran/edarf/f/vignettes/edarf.Rmd.... however am still confused to exactly how i should be using/understanding this variable. Any suggestions is greatly appreciated.
Thanks
"The parameter n is a numeric vector of length two, which controls via its first element the size of the grid to evaluate the columns specified by the vars argument at, and the second element gives the number of rows of the other variables to sample when marginalizing the prediction function. All the actual computation is performed in the mmpf package using the function marginalPrediction. Additional arguments can be passed to this function by name (i.e., using ...)."
Good evening,
I have a quick question about mle2() syntax. I have an optim() routine that optimizes a log-Likelihood function of the following form (and this runs thousands of times, so i don't want to change much):
ObjFun <- function(p, X, y, ModelFunction, CostFunction)
where p is a vector of 1-8 parameters, X is the input matrix, y is the response/independent variable vector, ModelFunction is a function specifying the shape of a model, and CostFunction specifies the cost/loss function the model likelihood should incorporate during the optimization. The code works fine with optim() or maxLik [maxLik] wit the following code:
maxLik(ObjFun, method="NM", start=c(1,2,3,4,5),
X=conc, y=y, ModelFunction=Model1, CostFunction=GCost)
constrOptim(init.par, ObjFun, ui = Ui, ci = Ci, method = "Nelder-Mead",
control = control1, X=X, y=y, ModelFunction= get(Model1),
CostFunction= get(GCost))
##i'm obviously using constrained optimization in my actual problem.
But I can't get it to work easily with mle() or mle2(). I just want to run it in mle2 to compare the likelihood profile with my own profiling function. Before i go digging through the mle2() code, does anyone know if it's my unnamed parameter vector or the additional arguments that make the function crash? I thought it was the former, but i am confused because the error it's giving me is:
mle2(ObjFun, method="Nelder-Mead"", start=c(1,2,3,4,5),
X=X, y=y, ModelFunction=Model1, CostFunction=GCost)
"minuslogl() : argument "ModelFunction" is missing, with no default"
and that argument is clearly specified. I couldn't really find any examples with additional parameters in the vignettes.
PS:
I would have just commented on this post as it's obviously related:
Creating function arguments from a named list (with an application to stats4::mle)
But I don't have enough points to comment.
UPDATE:
mle2() has options vecpar and parnames options that should allow one to specify "for compatibility with Optim", according to Ben's vignette. I simplified the objective function (the log-likelihood) to include hard-coded loss and model examples. The result looks like this:
mod2 <- mle2(ObjFun2, method="Nelder-Mead", start=list(1,2,3,4,5),
vecpar=T, parnames=c("A", "B", "C", "D", "E"))
However this still doesn't work. I have a hard time troubleshooting it because i don't know how to refer to the parameters inside the objective function after the call from mle2. If i include debugging commands such as print(p[2]) inside the ObjFun2, it returns NULL. So the parameter is no longer in a vector form. However print(A) forces the function to crush. Again, I can't find any working examples of this online, so maybe I'm missing something very simple.
I can't use the parameters argument as Ben suggested in the above link because my models are not linear.
I attempted to look inside the mle2() but got stuck on a call to calc_mle2_function().
I get the following error when I try to improve the intensity estimate of a kppm object, if I include the argument vcov = TRUE in the function improve.kppm:
Error in improve.kppm(object, type = type, rmax = rmax, dimyx = dimyx, :
object 'gminus1' not found
If I don't include the argument, the function runs but I cannot use the summary() function on the improved kppm object. I get the same error message as above. The same thing happens when I use vcov().
The call that I used to create my kppm object was (number of covariates has been reduced for clarity):
a05 = kppm(a2005nests ~ nest + nest2, cluster = "Thomas", covariates = fitcov(2))
where fitcov(2) is a function that returns a list of im objects. Could this be the issue? I've noticed that some spatstat functions on kppm objects throw errors if I used this function in the original kppm call. Usually it says something along the lines of Error: Covariates ‘nest’ and ‘nest2’ were not found.
There is a bug in the logical flow of improve.kppm: if vcov=TRUE and type != "quasi", the variable gminus1 is not defined. We will fix this in the development version of spatstat as soon as possible.
Did you perhaps select type="clik1" or type="wclik1" in the original call to kppm?
For the moment, you should be able to avoid the bug by omitting the argument type, or explicitly selecting type="quasi", in calls to kppm and improve.kppm.
The second problem, in which kppm fails to find the covariates, appears to be a scoping problem, but I can't reproduce it here. It would help if you can supply a minimal working example.
Im running an optimisation routine using optim in R and im telling the programme what i want returned. for example, if i put return(op1$par), it will return all 4 of my variable values. Thats fine, and if i run return(op1), I obviously get all the information from the optimisation routine (par, value, convergence etc). However, in this format, the par values arent accessible in the output, it simply details that there are 4 values.
Now what i need is to the get the parameter values and the convergence information at the same time. R wont let me call this return(op1$par, op1$convergence) so im looking for the best way to get these two entities in one run?
I should specify that im writing this to a file for 1000s of iterations and not just looking to call it up once on screen.
Cheers
Try something like this:
return(c(Parameters=op1$par, Convergence=op1$convergence))
The names Parameters and Convergence are only for identifying what are the parameters and what is the convergence, since this result will be a vector.
By design, a function can return only one object (or else assignments like a <- fn(b) would get confusing; which thing do you assign?). But that object can be a vector, or a list (which is what optim does). So wrap your arguments in something like
return(c(par=op1$par, convergence=op1$convergence))
or more generally (for objects of different types),
return(list(par=op1$par, convergence=op1$convergence))